Tuergen Yibulayin
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View article: Research on a denoising model for entity-relation extraction using hierarchical contrastive learning with distant supervision
Research on a denoising model for entity-relation extraction using hierarchical contrastive learning with distant supervision Open
Distant supervision is a technique that utilizes knowledge base information to automatically generate labels for text samples, enabling the large-scale creation of labeled data. However, this approach often encounters the issue of noisy la…
View article: Research on a Denoising Model for Entity-Relation Extraction Using Hierarchical Contrastive Learning with Distant Supervision
Research on a Denoising Model for Entity-Relation Extraction Using Hierarchical Contrastive Learning with Distant Supervision Open
Distant supervision is a technique that utilizes knowledge base information to automatically generate labels for text samples, enabling the large-scale creation of labeled data. However, this approach often encounters the issue of noisy la…
View article: Research on morphological knowledge-guided low-resource agglutinative languages-Chinese translation
Research on morphological knowledge-guided low-resource agglutinative languages-Chinese translation Open
Data sparsity and out-of-vocabulary are the main challenges in low-resource machine translation, and the impact of such problems in translation can be reduced through word segmentation. Word segmentation can be roughly divided into two cat…
View article: Genre: generative multi-turn question answering with contrastive learning for entity–relation extraction
Genre: generative multi-turn question answering with contrastive learning for entity–relation extraction Open
Extractive approaches have been the mainstream paradigm for identifying overlapping entity–relation extraction. However, limited by their inherently methodological flaws, which hardly deal with three issues: hierarchical dependent entity–r…
View article: Zero-Shot Relation Triple Extraction with Prompts for Low-Resource Languages
Zero-Shot Relation Triple Extraction with Prompts for Low-Resource Languages Open
Although low-resource relation extraction is vital in knowledge construction and characterization, more research is needed on the generalization of unknown relation types. To fill the gap in the study of low-resource (Uyghur) relation extr…
View article: PCA mix‐based Hotelling's T <sup>2</sup> multivariate control charts for intrusion detection system
PCA mix‐based Hotelling's T <sup>2</sup> multivariate control charts for intrusion detection system Open
Most of the data, which is in the field of network intrusion detection, have the characteristics of a mixture of high‐dimensional datasets of continuous and categorical variables. It easily leads the traditional multivariate control chart …
View article: Research on Uyghur Pattern Matching Based on Syllable Features
Research on Uyghur Pattern Matching Based on Syllable Features Open
Pattern matching is widely used in various fields such as information retrieval, natural language processing (NLP), data mining and network security. In Uyghur (a typical agglutinative, low-resource language with complex morphology, spoken…
View article: A Syllable-Based Technique for Uyghur Text Compression
A Syllable-Based Technique for Uyghur Text Compression Open
To improve utilization of text storage resources and efficiency of data transmission, we proposed two syllable-based Uyghur text compression coding schemes. First, according to the statistics of syllable coverage of the corpus text, we con…
View article: Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method
Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method Open
Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and automatic question answering system construction. However, relativel…
View article: Learning Subword Embedding to Improve Uyghur Named-Entity Recognition
Learning Subword Embedding to Improve Uyghur Named-Entity Recognition Open
Uyghur is a morphologically rich and typical agglutinating language, and morphological segmentation affects the performance of Uyghur named-entity recognition (NER). Common Uyghur NER systems use the word sequence as input and rely heavily…
View article: Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging
Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging Open
Uyghur is an agglutinative and a morphologically rich language; natural language processing tasks in Uyghur can be a challenge. Word morphology is important in Uyghur part-of-speech (POS) tagging. However, POS tagging performance suffers f…